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https://open.uns.ac.rs/handle/123456789/10798
Nаziv: | Design of adaptive Takagi-Sugeno-Kang fuzzy models | Аutоri: | Kukolj, Dragan | Dаtum izdаvаnjа: | 1-јан-2002 | Čаsоpis: | Applied Soft Computing Journal | Sažetak: | The paper describes a method of fuzzy model generation using numerical data as a starting point. The algorithm generates a Takagi-Sugeno-Kang fuzzy model, characterised with transparency, high accuracy and small number of rules. The training algorithm consists of three steps: partitioning of the input-output space using a fuzzy clustering method; determination of parameters of the consequent part of a rule from over-determined batch least-squares (LS) formulation of the problem, using singular value decomposition algorithm; and adaptation of these parameters using recursive least-squares method. Three illustrative well-known benchmark modelling problems serve the purpose of demonstrating the performance of the generated models. The achievable performance is compared with similar existing models, available in literature. © 2002 Elsevier Science B.V. All rights reserved. | URI: | https://open.uns.ac.rs/handle/123456789/10798 | ISSN: | 15684946 | DOI: | 10.1016/S1568-4946(02)00032-7 |
Nаlаzi sе u kоlеkciјаmа: | FTN Publikacije/Publications |
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